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Supplemental material, Table_S4_FGFR for FGFR aberrations increase the risk of brain metastases and predict poor prognosis in metastatic breast cancer patients by Ning Xie, Can Tian, Hui Wu, Xiaohong Yang, Liping liu, Jing Li, Huawu Xiao, Jianxiang Gao, Jun Lu, Xuming Hu, Min Cao, Zhengrong Shui, Yu Tang, Xiao Wang, Jianbo Yang, Zhe-Yu Hu and Quchang Ouyang in Therapeutic Advances in Medical Oncology

Supplemental material, Table_S3 for FGFR aberrations increase the risk of brain metastases and predict poor prognosis in metastatic breast cancer patients by Ning Xie, Can Tian, Hui Wu, Xiaohong Yang, Liping liu, Jing Li, Huawu Xiao, Jianxiang Gao, Jun Lu, Xuming Hu, Min Cao, Zhengrong Shui, Yu Tang, Xiao Wang, Jianbo Yang, Zhe-Yu Hu and Quchang Ouyang in Therapeutic Advances in Medical Oncology

Supplemental material, Table_S2 for FGFR aberrations increase the risk of brain metastases and predict poor prognosis in metastatic breast cancer patients by Ning Xie, Can Tian, Hui Wu, Xiaohong Yang, Liping liu, Jing Li, Huawu Xiao, Jianxiang Gao, Jun Lu, Xuming Hu, Min Cao, Zhengrong Shui, Yu Tang, Xiao Wang, Jianbo Yang, Zhe-Yu Hu and Quchang Ouyang in Therapeutic Advances in Medical Oncology

Supplemental material, Table_S5-brain for FGFR aberrations increase the risk of brain metastases and predict poor prognosis in metastatic breast cancer patients by Ning Xie, Can Tian, Hui Wu, Xiaohong Yang, Liping liu, Jing Li, Huawu Xiao, Jianxiang Gao, Jun Lu, Xuming Hu, Min Cao, Zhengrong Shui, Yu Tang, Xiao Wang, Jianbo Yang, Zhe-Yu Hu and Quchang Ouyang in Therapeutic Advances in Medical Oncology

The hybrid boundary node method (Hybrid BNM) is a boundary type meshless method which based on the modified variational principle and the Moving Least Squares (MLS) approximation. This paper apply the Hybrid BNM to thin structures. No plate and shell theories are used. Theory analysis of the feasibility of the Hybrid BNM for thin structures is proposed. Numerical result shows that the Hybrid BNM is suitable for thin structures and high accuracy can be observed.

We conducted an intensive survey of the Vulnerable eastern hoolock gibbon Hoolock leuconedys along the west bank of the Salween River in southern China, covering all known hoolock gibbon populations in China. We found 40–43 groups, with a mean group size of 3.9, and five solitary individuals. We estimated the total population to be < 200. In the nine groups for which we recorded composition, seven comprised one adult pair and 0–3 offspring and the other two groups both comprised one adult male and two adult females. The population is severely fragmented, in 17 locations, with the largest subpopulation containing only five family groups. Compared with the population in 1985 and 1994 five subpopulations have declined and gibbons have been extirpated from nine localities, although we discovered two previously unknown subpopulations. Commercial logging, illegal hunting, agricultural encroachment and population fragmentation pose serious threats to the future of H. leuconedys in China. An integrated conservation plan, including nature reserve establishment/expansion, enforcement of existing laws, conservation education, translocation and conservation-oriented research are needed to ensure the survival of H. leuconedys in China.

Various empirical works have shown that dispersion of firm-level profitability is significantly countercyclical. I incorporate firms' technology adoption decision into firm dynamics model with business cycle features to explain these empirical findings both qualitatively and quantitatively. The option of endogenous exiting and credit constraint jointly play an important role in motivating firms' risk taking behavior. The model predicts that relatively small sized firms are more likely to take risk, and that the dispersion measured as the variance/standard deviation of firm-level profitability is larger in recessions, which are consistent to the data.

Mean Shift algorithm is a robust approach toward feature space analysis and it has been used wildly for natural scene image and medical image segmentation. However, high computational complexity of the algorithm has constrained its application in remote sensing images with massive information. A fast image segmentation algorithm is presented by extending traditional mean shift method to wavelet domain. In order to evaluate the effectiveness of the proposed algorithm, multispectral remote sensing image and synthetic image are utilized. The results show that the proposed algorithm can improve the speed 5-7 times compared to the traditional MS method in the premise of segmentation quality assurance.